title: Sequence-based prediction of microRNA-binding residues in proteins using cost-sensitive Laplacian support vector machines.
abstract: The recognition of microRNA (miRNA)-binding residues in proteins is helpful to understand how miRNAs silence their target genes. It is difficult to use existing computational method to predict miRNA-binding residues in proteins due to the lack of training examples. To address this issue, unlabeled data may be exploited to help construct a computational model. Semisupervised learning deals with methods for exploiting unlabeled data in addition to labeled data automatically to improve learning performance, where no human intervention is assumed. In addition, miRNA-binding proteins almost always contain a much smaller number of binding than nonbinding residues, and cost-sensitive learning has been deemed as a good solution to the class imbalance problem. In this work, a novel model is proposed for recognizing miRNA-binding residues in proteins from sequences using a cost-sensitive extension of Laplacian support vector machines (CS-LapSVM) with a hybrid feature. The hybrid feature consists of evolutionary information of the amino acid sequence (position-specific scoring matrices), the conservation information about three biochemical properties (HKM) and mutual interaction propensities in protein-miRNA complex structures. The CS-LapSVM receives good performance with an F1 score of 26.23 ± 2.55% and an AUC value of 0.805 ± 0.020 superior to existing approaches for the recognition of RNA-binding residues. A web server called SARS is built and freely available for academic usage.
authors.affiliation: Nanjing University and Nanjing University of Posts and Telecommunications, Nanjing.
authors.forename: Jian-Sheng
authors.initials: JS
authors.lastname: Wu
authors.forename: Zhi-Hua
authors.initials: ZH
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D030562:Databases, Protein; D035683:MicroRNAs / Q000737:chemistry / Q000378:metabolism*; D056510:Position-Specific Scoring Matrices; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D020539:Sequence Analysis, Protein / Q000379:methods*; D060388:Support Vector Machine
chemical_list: D035683:MicroRNAs; D011506:Proteins